Promotion and demotion of posts in social networking services

ABSTRACT

Methods, systems, and apparatus, including computer programs stored on a computer storage medium, for determining a respective sampling rate for plurality of authors based on respective ranks assigned to the authors by a user, sampling one or more items for each of the authors according to the respective sampling rate for the author, and providing the sampled items for presentation to the user.

BACKGROUND

This specification generally relates to displaying content distributed to users in a social networking service.

Internet-based social networking services provide a digital medium for users to interact with one another and share information. For examples, users are able to distribute digital content (e.g., textual comments, digital images, digital videos, digital audio, hyperlinks to websites, etc.) to other users that they might be connected with in the social networking service. Digital content that is distributed to a user can be displayed to the user in a stream page.

As a user's social network within the social networking service increases, the amount of digital content distributed to the user and the amount of digital content that the user interacts with can significantly increase. Consequently, the user's stream page may overwhelm the user with digital content, which can include digital content that may not be relevant to the user and/or digital content that may be of low quality (e.g., spam).

SUMMARY

User interfaces can be enhanced by adding functionality to display less or more of content by a particular author, such as in a stream or news feed of a social network. A user interface can include “show more of” and “show less of” buttons next to a given author's name. Selection of one of the buttons will cause either more or less content of the given author to be displayed to the user. Repeated selection of a particular button will cause even more (or less) content of the given author to be displayed to the user. If some content of a given author is hidden, content by that author that is not hidden can provide a visual marker to indicate that there are more recent posts by that author that are hidden. User selection of the visual marker can cause the hidden content to appear in the stream or news feed of the user. Content of an author that would otherwise be hidden from a particular user can appear in a stream or news feed of the user when a topic of the content intersects with a topic of interest of the user or when the content is associated with an event such as a comment post or a “like”.

In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of obtaining a plurality of items for a user, each item comprising digital content and being associated with a respective author, a respective score, and a respective timestamp, wherein the respective score is based on, at least, a social score wherein the social score is determined based on a social affinity on a social networking service between the user and the respective author; determining a respective sampling rate for each of a plurality of different first authors based on a respective rank assigned to the first author by the user; sampling one or more of the plurality of items for each of one or more of the first authors according to the respective sampling rate for the first author wherein the sampled items for the first author each have a respective author that is the first author and a respective timestamp that is within a given time period; and providing the sampled items for presentation to the user. Other embodiments of this aspect include corresponding systems, apparatus, and computer programs.

These and other aspects can optionally include one or more of the following features. The sampling rate for a particular first author can increase as the rank assigned to the particular first author increases, and wherein the sampling rate for the particular first author can decrease as the rank assigned to the particular first author decreases. One or more of the sampled items can each be associated with one or more events. An event can comprise one of a comment on a particular item, an endorsement of the particular item, a muting of the particular item, or a share of the particular item. The sampled items can each pertain to one or more topics of interest to the user. The sampled items for the first author can each have a respective score that is beyond a score threshold. The respective score of a particular item can further be based on, at least, a relevance score wherein the relevance score is determined based on a relevance of the particular item to the user. The sampled items can be ordered before providing them for presentation to the user, wherein the sampled items are ordered according to, at least, respective timestamps and respective scores of the sampled items. The digital content can comprise digital content distributed using a computer-implemented social networking service. The second items can be presented to the user in a stream page of a social networking service.

The details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other potential features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example network architecture.

FIG. 2 is a diagram of an example social network including social circles.

FIG. 3 depicts a screen-shot of example graphical user interface for creating and maintaining social circles.

FIG. 4 depicts a screen-shot of an example stream page for a user of a social networking service.

FIG. 5 depicts an example system for ranking items displayed in a stream page.

FIG. 6 depicts example ranking and ordering of example items in a stream page for a user.

FIG. 7 is a flow chart of an example process that can be executed in implementations of the present disclosure.

FIG. 8 is a flow chart of an example process that can be executed in implementations of the present disclosure.

FIG. 9 is a flow chart of an example process that can be executed in implementations of the present disclosure.

Like reference numbers represent corresponding parts throughout.

DETAILED DESCRIPTION

In general, implementations of the present disclosure are directed to displaying items in stream pages of users of a social networking service. In particular, implementations of the present disclosure are directed to a rank assigned to a particular author by a user that will cause either more or less content of the author to be displayed in the user's stream page. The stream page displays digital content that has been posted by the user to the social networking service and/or digital content distributed to the user through the social networking service. In some examples, each item that is to be displayed in the stream page of a user is associated with an item score and a timestamp. Items are displayed within the stream page of the user based on the item scores and timestamps. In some examples, items are grouped based on timestamp. In some examples, items within groups are ranked based on item score. Items are displayed in the stream page of the user based on group and in order within a group based on rank. In this manner, the most relevant, highest quality items are displayed more prominently to the user.

In some examples, the timestamp for an item is determined based on one or more events associated with the item. Example events can include original distribution of the item (e.g., when the item was posted to the social networking service), a comment appended to the item (e.g., when a user (either the author of the item or non-author of the item) posts a comment to the item), an endorsement of the item (e.g., when a user (either the author of the item or non-author of the item) indicates that they endorse the item), sharing of the item (e.g., when a non-author re-posts, or otherwise redistributes the item within the social networking service).

In some examples, a timestamp for the item can be determined based on weighting applied to events as they occur. For example, an item weight can be set to an initial value (e.g., 0) when the item is first distributed (e.g., when the author generates and distributes the item). The item weight can be incremented based on each event. The degree to which the item weight is incremented can vary based on event type and/or characteristics of the event. For example, a comment to the item can increment the item weight by a first value and an endorsement of the item can increment the item weight by a second value. In some examples, the first value can be exceeds the second value, indicating that a comment is considered to be more substantial than an endorsement. In some examples, the first value can be based on a social affinity (discussed further below) between the author of the item and the user posting the comment to the item. For example, the first value is higher for a user that has a high social affinity to the author than for a user that has little, if any, social affinity to the author. The item weight can be compared to a threshold item weight. When the item weight exceeds the threshold item weight, the timestamp of the item is set to the timestamp of the most recent event.

In some implementations, the threshold item weight for a particular item can vary over time. In some examples, the threshold item weight can gradually increase over time. In this manner, a timestamp associated with the particular item is not updated over and over again to inhibit recurring bumping of the particular item higher within stream pages in long conversations (e.g., numerous comments posted to the item). In some examples, the threshold item weight can be increased on a per-user basis. For example, for a user that has generated an event associated with the item, the threshold item weight can be increased (e.g., based on a number of items the user has interacted with).

In some examples, each item includes an associated item score on a per recipient basis. For example, an author user (or “author”) can generate the item and can distribute the item to a plurality of users of the social networking service including a first user and a second user. A first item score can be associated with the item and can be determined based on a social affinity between the first user and the author, a social affinity between the first user and the other users to which the item was distributed, a social affinity between the first user and other users interacting with the item (e.g., commenting on the item, endorsing the item, sharing the item), an interaction of the first user with the item (e.g., commenting on the item, endorsing the item, sharing the item), item content and/or quality associated with the author. A second item score can be associated with the item and can be determined based on a social affinity between the second user and the author, a social affinity between the second user and the other users to which the item was distributed, a social affinity between the second user and other users interacting with the item (e.g., commenting on the item, endorsing the item, sharing the item), an interaction of the second user with the item (e.g., commenting on the item, endorsing the item, sharing the item), item content and/or quality associated with the author. The first score can be different from the second score. Consequently, the item can include an associated timestamp and associated item scores, each of the associated items scores being particular to a non-author.

A social affinity between a particular user of the social networking service and other users of the social networking service can be determined based on a social graph of the particular user. In some examples, a social graph can refer to a single social graph or multiple interconnected social graphs. Distinct social graphs can be generated for different types of connections a user has. For example, a user can be connected with chat contacts in one social graph, email contacts in a second social graph, and connections (or contacts) from a particular social networking service in a third social graph. Each social graph can include edges to additional individuals or entities at higher degrees of separation from the user. These contacts can in turn have additional contacts at another degree of separation from the user. Similarly, a user's connection to someone in a particular social network can then be used to identify additional connections based on that person's connections. The distinct social graphs can include edges connecting one or more social graph to one or more other social graphs.

Types of connections and social graphs can include, but are not limited to, other users in which the user is in direct contact (e.g., user mail or chat contact, direct contacts on social sites) and users in which the user is in indirect contact (e.g., friends of friends, connections of users that have a direct connection to the user). In some implementations, the social graph includes content generated by individuals (e.g., blog posts, reviews) as connections to the user. The social graph can include connections within a single network or across multiple networks (separable or integrated). Public social graph relationships can also be considered. In some examples, public relationships can be established through public profiles and/or public social networking services.

A social affinity score can be determined for a connection between a user and another user. The social affinity score can reflect strength of a relationship between the users. By way of non-limiting example, a first user can be connected to a second user and to a third user. The first user and the second user can be in frequent contact with one another via the social networking service (e.g., often sharing items with one another), email and/or chat. A first social affinity score can be provided based on the interaction between the first user and the second user. The first user and the third user might be infrequently in contact with one another. For example, the first user might have received an email from the third user only once in the last several months. A second social affinity score can be provided based on the interaction between the first user and the third user. In this example, the first social affinity score exceeds the second social affinity score, reflecting a closer relationship between the first user and the second user than between the first user and the third user.

In some implementations, the social affinity score can be computed using a linear combination of features. The weights for the individual features can be manually tuned or adjusted using machine learning techniques such as linear regression, support vector machines, or neural networks, for example. Features can be positive (i.e., they will increase the social affinity) or negative (i.e., they will decrease the social affinity). The set of features can include, for example, any combination of the following features.

Positive Features:

-   -   User A has user B in user A's social circles. (Social circles         are described below.)     -   User B has user A in user B's social circles (these are         asymmetric relations that might have different weights).     -   User A frequently visits user B's profile.     -   User B frequently visits user A's profile (asymmetric as well).     -   User A has email contact with user B (or vice versa).     -   Users A and B have chatted in the past.     -   Users A and B write posts in the same language.     -   Users A and B have many joint contacts (through any of their         different social graphs).

Negative Features:

-   -   User A has blocked user B (or vice versa).     -   User A has marked emails/chat messages/posts of user B as spam.

For purposes of illustration, an example social networking service will be described. The example social networking service enables users to organize contacts into social circles. It is appreciated, however, that implementations of the present disclosure can be used in other types of social networking services and are not limited to social networking services that include social circles.

In general, social circles are categories to which a user can assign contacts and better control the distribution and visibility of social networking posts and/or other digital content distributed using the social networking service. In some implementations, a social circle can be provided as a data set defining a collection of contacts that are associated with one another in a computer-implemented social networking service. Generally, a social circle can be described from the perspective of an individual that is the center of a particular collection of socially interconnected people, or from the aggregate perspective of a collection of socially interconnected people. A social circle can have narrowly defined boundaries (e.g., members of the social circle might be familiar with one another) and permission may be required for a member to join a social circle. A user of the social networking service can define a social circle. The social circle, as a data set defining a collection of contacts, may reflect real-life social connections and/or interactions of the user. In some implementations, a social circle can be defined by a user as a personal representation or grouping of a set of contacts, the contacts may be unaware of the social circle and/or unaware of other members of the social circle.

Through the creation and use of social circles, the user can organize and categorize social networking contacts into various different groupings that can be used to control the visibility and access those contacts have to the user's postings, digital pictures, videos, audio files, hyperlinks (e.g., uniform resource indicators, URIs), and/or other content associated with the user's social networking profile. As one example, the user can post an update about a work-related nuance to only a “coworker” circle, and spare other contacts within the social networking service from seeing information that is irrelevant to them.

FIG. 1 is a diagram of an example network architecture 100. The network architecture 100 includes a number of client devices 102, 104, 106, 108, 110 communicably connected to a server system 112 by a network 114. The server system 112 includes a processing device 116 and a data store 118. The processing device 116 executes computer instructions (e.g., social network computer program code, and/or IM computer program code) stored in the data store 118 to perform the functions of a social network server and/or instant messaging (IM) server.

Users of the client devices 102, 104, 106, 108, 110 access the server device 112 to participate in a social networking service. For example, the client devices 102, 104, 106, 108, 110 can execute web browser applications that can be used to access the social networking service and/or an IM service. In another example, the client devices 102, 104, 106, 108, 110 can execute software applications that are specific to the social networking service and/or the IM service (e.g., social networking and/or IM “apps” running on smartphones).

Users interacting with the client devices 102, 104, 106, 108, 110 can participate in the social networking service provided by the server system 112 by digital content, such as text comments (e.g., updates, announcements, replies), digital images, videos, audio files, and/or other appropriate digital content. In some implementations, information can be posted on a user's behalf by systems and/or services external to the social networking service or the server system 112. For example, the user may post a review of a movie to a movie review website, and with proper permissions that website may cross-post the review to the social networking service on the user's behalf. In another example, a software application executing on a mobile device, with proper permissions, may use global positioning system (GPS) capabilities to determine the user's location and automatically update the social network with his location (e.g., “At Home”, “At Work”, “In Brownsdale, Minn.”). Generally, users interacting with the client device 102, 104, 106, 108, 110 can also use the social networking service provided by the server system 112 to define social circles to organize and categorize the user's relationships to other users of the social networking service. Examples of the creation and use of social circles are provided in the description of FIG. 2.

In some implementations, the client devices 102, 104, 106, 108, 110 can be provided as computing devices such as laptop or desktop computers, smartphones, personal digital assistants, portable media players, tablet computers, or other appropriate computing devices that can be used to communicate with an electronic social network. In some implementations, the server system 112 can be a single computing device such as a computer server. In some implementations, the server system 112 can represent more than one computing device working together to perform the actions of a server computer (e.g., cloud computing). In some implementations, the network 114 can be a public communication network (e.g., the Internet, cellular data network, dialup modems over a telephone network) or a private communications network (e.g., private LAN, leased lines).

FIG. 2 is a diagram of an example social network 200 including social circles. A user 202 is a member of a social network that supports the creation and use of social circles (e.g., the social network provided by the server device 112 of FIG. 1). In the present example, the user 202 has a number of contacts 204 a-204 i with which the user 202 can have some form of relationship (e.g., friends, coworkers, customers, teammates, clients, relatives, club members, classmates). The user 202 categorizes the contacts 204 a-204 i by assigning them to one or more social circles, such as a social circle 210, a social circle 220, and a social circle 230.

The social circle 210 is a personal circle. In some implementations, personal circles are groupings created by and may be known only to the user 202 (e.g., the contacts 204 a, 204 b may receive no indication that they are in the user's 202 personal social circle 210). In some implementations, personal social circles are groupings created by the user 202 and may be known to the user 202 as well as the contacts (e.g., contacts 204 a, 204 b) that are members of the social circle (e.g., the contacts 204 a, 204 b receive an indication that they have been added to the personal social circle 210).

In some implementations, personal circles may be used to organize and categorize the contacts 204 a-204 i in ways that are relevant to the user 202. In some implementations, the user 202 may use personal social circles to organize contacts in order to discretely target which of his contacts 204 a-204 i will see certain postings or have access to particular information. For example, the user 202 may be planning a surprise party for a small group of friends. As such, the user can organize contacts into “Surprise Party Attendees” and “Surprise Party Honorees” personal circles. By doing so, the user 202 may better target selected postings to the friends attending and/or helping to plan the surprise party (e.g., Surprise Party Attendees), while targeting selected postings to friends that are to be honored at the surprise party (e.g., Surprise Party Honorees) to maintain the integrity of the surprise.

The social circle 220 is a shared private circle, which may also be referred to simply as a shared circle. In general, shared private circles are social circles that the user 202 creates and invites contacts to voluntarily join. Contacts that accept the invitation become members of the shared private circle. Members of a shared private circle can see information posted to that circle by the user 202 and can post information to be shared with other members of the shared private circle. For example, the user 202 may tend to post a large number of jokes to the social network. However, while some of the contacts 204 a-204 i may find the jokes to be entertaining, others may find them to be simply annoying. Realizing this, the user 202 may create a “jokes” shared private circle and invite some or all of the contacts 204 a-204 i to join. With the “jokes” social circle in place, the user 202 may post witticisms to the “jokes” circle, and only those contacts who have accepted the invitation are able to see the comicality of the user 202. Similarly, members of the shared private circle are able to post messages to the circle, and those posts are visible to other members of that circle.

The social circle 230 is a shared public circle. In general, shared public circles are social circles that the user 202 creates, and invites contacts to voluntarily join. Further, the existence of a shared public circle is publicly available such that other users of the social networking service (e.g., not necessarily just the user's 202 contacts 204 a-204 i) may request to join the public social circle. Members of shared public circles may post information to, and see updates posted by, other members of the same public shared circle. In some implementations, public shares circles may be “fan” or “group” circles (e.g., circles dedicated to a particular place, event, product, movie, celebrity, sports team, company, concept, philosophy, organization, support network). For example, the user 202 may create a shared public circle for his band, and fans of his act may join the circle to discuss upcoming shows, download MP3s of the band's music, or post videos from recent concerts. In another example, the user 202 may create a shared public circle for alumni of his high school graduating class, which his former classmates may find and join in order to stay in touch with one another and post pictures from their school days. Once a shared public circle is created, in some implementations the user 202 can invite people to join the circle. In some implementations, nonmembers of the circle can request membership in the shared public circle, and membership in a shared public circle may be automatic upon request, or may require the user's 202 approval to become members of the shared public circle.

In some implementations, one or more default social circles can be provided or suggested to a user when the user subscribes to a social networking service. For example, “Friends,” “Family,” and “Coworkers” social circles can automatically be provided in a user's profile upon the user subscribing to the particular social networking service. Other social circles can automatically be provided including, for example, an “Acquaintances” social circle and/or a “Just Following” social circle. In some implementations, the automatically created or suggested social circles can include personal social circles. Although default social circles can be automatically provided, it may be left to the user to actually populate the default social circles with contacts. For example, each of the default social circles may initially be empty of contacts, and the user populates each of the default social circles as discussed in further detail herein.

As discussed herein, digital content can be distributed to contacts within the social networking service including one or more social circles, such that they are exclusively viewable by the indicated contacts and/or contacts within one or more indicated social circles. For example, a user of the social networking service can generate a post and indicate one or more social circles for distribution of the post. In some implementations, an auto-complete component enables the user to type in part of the name of a social circle and/or individual contact to specify which social circles and/or individual contacts require delivery of the post content. During a post write-time, a post data set is transmitted from the user's client computing device (e.g., client device 102 of FIG. 1) to a distribution hub, which can be provided at a server (e.g., server system 112 of FIG. 1). The post data set includes a plurality of data. In some implementations, the post data set includes post content data (e.g., text, uniform resource indicator (URI)), timestamp data (e.g., a timestamp indicating the time that the post was generated), distribution data (e.g., contacts and/or one or more social circles), and identification (ID) data (e.g., an ID assigned to the post data set upon generation of the post).

In some implementations, and as discussed in further detail herein, other data can be appended to post data sets. Example other data can be spam indication data and scoring data. In some examples, spam indication data can be appended to a post data set to indicate that the post data set is considered to be spam. In some examples, scoring data can include a social affinity score among other possible scoring data.

In some implementations, the scoring data can be recipient specific. In some examples, the scoring data can include social affinity data that is provided based on respective social affinity scores between an author of the post and each recipient of the post. For example, a first user can author a post and define a distribution of the post, creating an underlying post data set. The distribution can include a second user and a third user. A first social affinity score associated with a social relationship between the first user and the second user can be provided, and a second social affinity score associated with a social relationship between the first user and the third user can be provided. The first social affinity score can be different from the second social affinity score, reflecting different social relationships between the first user and the second user and the first user and the third user. The first social affinity score and the second social affinity score can be appended to the post data set and/or stored in another location while maintaining an association with the post data set.

In some implementations, the distribution data is processed to provide an access control list (ACL) that specifies which contacts within the social networking system are granted access to the post content. In some implementations, social affinity scores can also be processed to provide the ACL. For example, the distribution data can indicate that the post content is to be accessible by and/or distributed to a particular user. A social affinity score can be determined for the post and can be specific to a relationship between the post recipient and the author of the post. In some examples, if the social affinity score is below a threshold score, it is determined that the post content will not be accessible by and/or distributed to the particular user. Consequently, although the particular user had been explicitly targeted for distribution of the post by the author, the particular user is not provided in the ACL for the post. In some examples, if the social affinity score is at or above the threshold score, it is determined that the post content will be accessible by and/or distributed to the particular user. Consequently, the particular user had been explicitly targeted for distribution of the post by the author and the particular user is provided in the ACL for the post.

Generally, the distribution hub determines end points the post data set is to be distributed to based on the ACL. More specifically, the set of contacts that may care about the post and/or that are allowed access to the post is determined based on the ACL, and the ID of the post is written to a per user/view index at the distribution hub. When fetching posts to distribute to a user, the user/view index is accessed and the IDs of the various posts that the user is allowed to view are determined. The post data sets are retrieved from a data store (e.g., data store 118 of FIG. 1) and are transmitted to a client device associated with the requesting user. In some implementations, and as discussed in further detail herein, the ACL can be provided based on the overall score, the quality score and/or the social affinity score.

FIG. 3 depicts a screen-shot 300 of an example graphical user interface for creating and maintaining social circles. In the screen-shot 300, a social graph editor user interface (UI) 301 is shown. In some implementations, the UI 301 can be the interface presented by a purpose made social networking application, while in some implementations the UI 301 can be one or more web pages of a social networking website displayed in a general purpose web browser.

In the example of FIG. 3, the UI 301 includes a number of choices presented in a menu bar 305. In the present example, the “Social Network” choice has been selected by a user. This selection causes a web-based social networking application to be executed and a social network menu 310 to be displayed. The social network menu 310 includes a profile indicator 312 in which information such as a user name 314 and a user image 316 associated with the currently logged in user are displayed.

The social network menu 310 also displays, among other items, a social circles sub-menu 318. The social circles sub-menu 318, when selected (e.g., as represented by the highlighting of the submenu's title), causes a social circle display 320 to be presented. The social circle display 320 includes a number of circles 322 a-322 e that are visual representations of various social circles that the user has created or has permission to edit. Each of the circles 322 a-322 e displays information about the social circle it represents. For example, the circle 322 c displays a name 324 a, a count 324 b of the number of contacts associated with the social circle, and an indication 324 c of what kind of circle (e.g., personal, private shared, public shared) that the circle 322 c is.

The social circle display 320 also includes a contact display 326. The contact display 326 provides a graphical interface for viewing, selecting, and organizing items in the user's contact lists. A collection of contact icons 328 a-328 i represents the contacts or other entities (e.g., organizations, places, or other items) socially networked with the particular user. In some implementations, the icons can be digital photos of the contacts they represent (e.g., the icons 328 a, 328 d), arbitrary images (e.g., the icons 328 b, 328 g), or placeholders (e.g., when the contact has no image associated with their account, such as the icon 328 c). In some implementations, the icons can include additional information, such as the names of each contact. A scroll bar 329 is provided for the user to view additional contact icons that may not fit into the initial view.

FIG. 4 depicts a screen-shot of an example stream page 400 for a user of a social networking service. For example, the stream page 400 can be provided as a web page within a website of a social networking service, and can display digital content that has been shared with a user associated with the stream page 400. In the illustrated example, the example user is “Rob McDavies” and the stream page 400 displays items (e.g., digital content) that other users have shared with the user and/or items that the user has shared with other users. Status area 414 presents a summary of the users who are sharing items with the user. Menu 402 item 412 directs the social networking service to display incoming items in the stream page 400. The stream page 400 includes a social network menu 402 and an item region 404 containing items 406 and 408.

Example items 406 and 408 that have been distributed to the user are displayed in the item region 404. Generally, the items 406, 408 displayed in the item region 404 include digital content that is distributed to the user from contacts established within the social networking service. A content sharing interface 410 can also be provided in the item region 404. The user can activate (e.g., click on) the content sharing interface 410 to share digital content. Although two items 406, 408 are depicted in FIG. 4, it is appreciated that the stream page 400 can display any number of items to the user.

The stream page 400 includes functionality to allow the user to display less or more of content by a particular author in the item region 404. In some implementations, an item (e.g., item 406 or item 408) can include “show more of” and “show less of” interactive buttons located near the item author's name. (Alternatively, similar functionality can be accessible elsewhere on the stream page 400 or on another user interface.) By way of illustration, near the author Katie's name for item 406 is a “Show less Katie” button 416 and a “Show more Katie” button 418. User selection of the button 416 will cause a user-specific rank (or other measure) of the author Katie to decrease whereas selection of the button 418 will cause Katie's rank to increase. The author rank is user-specific because a different user can assign a different author rank for a given author. Repeated selection of either button 416 or 418 will cause further decreases (or increases) to Katie's rank. The maximum value of the author rank can be zero meaning that none of the author's items will be hidden from the user. For example, each time the “Show less” button (e.g., button 416) is selected, the user-specific author rank of Katie will be decreased by an amount (e.g., 1). Likewise, each time the “Show more” button (e.g., button 418) is selected, the user-specific author rank of Katie will be increased by an amount (e.g., 1) up to the maximum value of zero. Other ways of determining an author's rank are possible.

For a given user, the user-specific author rank determines how many of an author's items will be displayed to the user such as, for example, in the stream page 400 of the user. In some implementations, the user-specific author rank is used to determine a sampling rate. The sampling rate is used to select items by the author for inclusion in the stream page 400 of the user. For example, the sampling rate can specify that every Nth item of the author is selected in a given time period. By way of illustration, if a given author's rank is initially zero then N=1. If a given author's rank is −5 then N=5. Similarly, if a given author's rank is −7 then N=7. Other ways of determining the sampling rate are possible. Sampling of an author's items can be random meaning that the first item selected for inclusion in a user's stream page 400 is selected randomly and every Nth item thereafter is selected. Alternatively, if the author's items are ordered in some fashion, the first item selected for inclusion in the user's stream page 400 can be the first item in the order and then every Nth item thereafter is selected. In further implementations, sampling can be performed by “relevance”, e.g. for every user, the items are ranked according to some relevance score (RS) and then the top N items are chosen, where N is chosen proportional to the amount of items to be sampled. Other sampling techniques are possible.

In further implementations, the stream page 400 includes functionality to allow the user to display less or more of content regarding a particular topic of interest. In some implementations, an item (e.g., item 406 or item 408) can include “show more posts like this” and “show fewer posts like this” interactive buttons located near an item's content. (Alternatively, similar functionality can be accessible elsewhere on the stream page 400 or on another user interface.) Selection of these buttons indicates that the user is interested in seeing more (or less) items regarding the topic of the item. The topic of an item can be determined by, for example, automatically identifying the central ideas in a text based on a knowledge-based concept counting. Other ways of determining the topic of an item are possible. Items that have a topic that a user wants to see more of can have their ranks increased. Likewise, items that have a topic that the user wants to see less of can have their ranks decreased.

In further implementations, items for a given author can be omitted from the author's sampled items if the items have respective time stamps that are outside of a given time period (e.g., the most recent 24 hours), do not pertain to a topic of interest to the user (e.g., golf), contain certain words or phrases (e.g., profanity), are not associated with one or more events (e.g., a comment, an endorsement, a muting, or a share), or have respective scores that do not satisfy a score threshold. In some implementations, a score threshold can be determined automatically. For example, given a set of N scores, and a target number of M items (where N>M) the target threshold θ is determined such that M scores are higher than the threshold θ. An item's score can be based on a social score wherein the social score is determined based on social affinity on a social networking service between the user and the respective author of the first item. The social score can incorporate whether two users have similar social circles and/or whether user A has put user B into multiple circles, for instance. An item's score can also be based on a relevance score wherein the relevance score is determined based on a relevance of the item to the user. Other types of scores are possible. Calculation of item scores is discussed further below.

In some implementations, items for a given author that are not sampled can be accessed by users through a button or other user interface control that allows the author's non-sampled items to be shown (e.g., in the stream page 400 of the user). For example, one or more items of author Sam were not sampled for display in the stream page 400. A “Show Sam's hidden posts” button 424 presented in item 406 will, when selected by a user, cause display of one or more of items authored by Sam that were not sampled for display in the stream page 400.

Implementations of the present disclosure are directed to determining which items and in what order to display to the user within a stream page of the user of the social networking service. As discussed above, each item that is distributed to the user includes an associated timestamp and an item score. When the user logs into the social networking service, a user-specific index of items that have been distributed to the user can be accessed. The index of items indicates items that can be displayed to the user within the stream page of the user. For each item in the index of items, a timestamp and an item score are provided. In some examples, the items are filtered based on item score, to remove items having scores that are less than a threshold score from being displayed to the users. The items are grouped based on timestamp. For each group, items within the group are put in rank order based on item score. The items are displayed to the user by group in ascending order of time and by the rank order within each group, as discussed in further detail herein.

In some implementations, each group can include an associated time range. By way of non-limiting example, a first group can include timestamps more recent than the last 24 hours, a second group can include timestamps within the last 1 day to 2 days, a third group can include timestamps within the last 2 days to 7 days, and a fourth group can include timestamps within the last 7 days to 1 month. It is appreciated that the described groups are merely examples and additional and/or different groups can be provided.

In some implementations, each item can be grouped based on a difference between a current time and the timestamp of the item. In some examples, the current time can include the time at which items are to be retrieved for display to the user (e.g., when the user logs into the social networking service, when the user clicks on the stream page, when the user refreshes the stream page, when the stream page is automatically refreshed).

As discussed above, the timestamp for an item can be determined based on weighting applied to events as they occur. For example, an item weight can be set to an initial value (e.g., 0) when the item is first distributed (e.g., when the author generates and distributes the item). The item weight can be modified, e.g., incremented, based on each event type. Example event types can include comment (e.g., the author or a non-author posts a comment to the item), endorsement (e.g., the author or a non-author endorses the item) and sharing (e.g., the author or a non-author redistributes or otherwise shares the item). For example, a comment to the item can increment the item weight by a first value and an endorsement of the item can increment the item weight by a second value. In some examples, the first value can be exceeds the second value, indicating that a comment is considered to be more substantial than an endorsement. The degree to which the item weight is incremented can also vary based on one or more characteristics of the event. An example characteristic can include a social affinity between the author of the item and a non-author performing the event. For example, the first value, discussed above, is higher for a user that has a high social affinity to the author than for a user that has little, if any, social affinity to the author.

Upon the occurrence of an event, the item weight is incremented and is compared to a threshold item weight. When the item weight exceeds the threshold item weight, the timestamp of the item is set to the timestamp of the most recent event. For example, an item includes a first item weight that is less than the threshold item weight. A user posts a comment to the item at timestamp t. Consequently, an event of type comment occurred to the item at timestamp t. The first item weight is incremented to a second item weight. The second item weight exceeds the threshold item weight. Consequently, the timestamp of the item is set equal to timestamp t, which is the timestamp of the event that resulted in the second item weight.

Data associated with the item, including the item weight and the timestamp, can be stored in an item repository (e.g., a database) and the data can be periodically updated. In some examples, the data is updated in response to the occurrence of an event to the item. The data associated with the item can be retrieved for ranking and ordering of the item within a stream page of a user, as discussed in further detail herein.

As noted above, items that are to be displayed to a user are organized into groups. Within each group, the items are put in an order based on item score. The item score (S) reflects a quality of the item and a relevance of the item to the particular user. In some implementations, the item score (S) is determined based on a quality score (QS), a social score (SS) and a relevance score (RS). In some implementations, the quality score is determined based on an author status score (VIP) and a content score (CS). Each of the scores is discussed in further detail below.

In some implementations, the item score is determined as a maximum of a first value and a second value. In some examples, the first value and the second value can be determined based on the quality score (QS), the social score (SS) and a smoothing function. In some examples, the first value is determined based on applying the smoothing function to SS and multiplying the result by QS. In some examples, the second value is determined based on applying the smoothing function to QS and multiplying the result by SS. In some examples, the item score is determined as a product of the maximum of the first value and the second value and the relevance score (RS). It is appreciated that the item score (S) can be determined based on other relationships between the quality score (QS), the social score (SS) and the relevance score (RS). For example, the item score (S) can be provided as the sum of the quality score (QS), the social score (SS) and the relevance score (RS).

The quality score (QS) is determined based on the status score (VIP) and the content score (CS). In some implementations, the quality score (QS) is determined as the sum of the status score (VIP) and the content score (CS). In some examples, weights are applied to each of the status score (VIP) and the content score (CS). In this manner, respective influences of the status score (VIP) and the content score (CS) on the quality score (QS) can be provided. For example, one or both of the status score (VIP) and the content score (CS) can be weighted such that the content score (CS) has a greater influence on the quality score (QS). It is appreciated that the quality score (QS) can be determined based on other relationships between the status score (VIP) and the content score (CS).

The status score (VIP) is an author-specific score and is determined based on characteristics of the author of the item within the social networking service. The status score can be based on one or more of the number of asymmetrical relationships that the author has within the social networking service, how often the author distributes items within the social networking service, how often other users interact with items distributed by the author (e.g., comment on, endorse, share), and how often the author interacts with other users of the social networking service. For example, a celebrity or a well-known blogger may have a status score that is greater than status scores of less known users of the social networking service.

To illustrate the status score (VIP), and by way of non-limiting example, a first author distributes a first item and a second author distributes a second item. The first author is a prolific user of the social networking service, receives a significant number of events to items that the first author distributes and is highly interactive with other users of the social networking service has a lot of followers. The second author rarely distributes items within the social networking service, receives little to no events to items that the second author does distribute and does not often interact with other users of the social networking service. In this example scenario, the status score of the first author is higher than the status score of the second author.

The content score (CS) is an item-specific score and is determined based on interactions of other users with the particular item. In some examples, the content score is determined based on a number of events to the item and the type of events. In some examples, negative events can also be considered in determining the content score. An example negative event can include one or more users muting the particular item, such that the item is no longer displayed in their respective stream pages. In some examples, the content score can be provided as a sum of a number of comments to the item, a number of endorsements of the item, a number of shares of the item and a number of mutes of the item. In some examples, respective weights can be applied to define an influence that each of the number of comments to the item, the number of endorsements of the item, the number of shares of the item and the number of mutes of the item has on the value of the content score. In some examples, the amount of content can influence the content score.

The social score (SS) is a user-specific, item-specific score and is determined based on a social affinity between the user that may be receiving the item in a stream page and the author of the item. In some implementations, the social score is further determined based on social affinities between the user and other users to which the item was also distributed and that may have interacted with the item (e.g., commented on the item, endorsed the item, shared the item). In some examples, the social score can be provided as a sum of social affinity scores. For example, the social score can be provided as the sum of a social affinity score reflecting a social affinity of the user and the author and a plurality of social affinity scores reflecting respective social affinities between the user and other users that have interacted with the item. In some examples, one or more weights can be applied to the respective social affinity scores to define an influence that each of the social affinity scores has on the social score. In some examples, the social affinity score between the user and the author can be weighted more heavily than the social affinity scores between the user and other users that have interacted with the item.

To illustrate the social score (SS), and by way of non-limiting example, an author distributes a first item and a second item. The first item is distributed to a first set of users including a subject user. The second item is distributed to a second set of users also including the subject user. The subject user is a direct contact of each of the other users within the first set of users. The subject user is not a direct contact of any of the other users within the second set of users. A first social score is provided for the first item and a second social score is provided for the second item. The first social score and the second social score are each specific to the subject user. The first social score is determined based on a social affinity between the subject user and the author and respective social affinities between the subject user and each of the other users in the first set of users. The second social score is determined based on the social affinity between the subject user and the author and respective social affinities between the subject user and each of the other users in the second set of users. The first social score is greater than the second social score, because the subject user has higher social affinities to the other users in the first set of users than to the other users in the second set of users, the subject user being a direct contact of each of the other users in the first set of user.

The relevance score (RS) is a user-specific, item-specific score and is determined based on a relevance of the particular item to the user. In this manner, matching interests between the user and the subject of the item can be accounted for. In some examples, interests of the user can be determined explicitly and/or implicitly. In some examples, the user can explicitly identify interests within the social networking service. For example, the social networking service can provide a tool or service to promote discussion on certain topics relevant to the user. The user can provide topics that they are interested in (e.g., politics, green technology, sports) and other users can interact with the user to discuss each of the user-provided topics. As another example, the user's interactions with items distributed by other users within the social networking service can imply topics that the user is interested in. For example, the user often comments on, endorses and/or shares items that relate to politics, but rarely comments on, endorses or shares items that relate to sports. Consequently, it can be implied that the user is interested in politics and might not be interested in sports.

In determining the relevance score (RS), a topic of the item can be determined. In some examples, the topic of the item can be determined based on data underlying the item. For example, the item can include a link to a uniform resource locator (URL). The URL can be accessed and data provided at the URL can be reviewed to determine whether the URL corresponds to one or more topics. Using sports as an example topic, the URL can be a link to a sports news service or a sports team website. Consequently, it can be determined that the item relates to sports. As another example, the item can include text provided by the author. The text can be reviewed to determine whether the text corresponds to a specific topic. Again using sports as an example topic, the text can include “Are we getting together to watch the Stanley Cup finals?” Consequently, it can be determined that the item relates to sports, ice hockey in particular.

In some examples, one or more topics determined for the item can be compared to interests of the user. If there is no match between topic(s) and interest(s), the relevance score can be set equal to zero. If there are one or more matches, the relevance score can be set equal to a value greater than zero.

In some implementations, the relevance score can be determined based on the item contents and URL characteristics. In some examples, a content feature vector can be generated and can include a list of terms with corresponding weights. For example, ice-hockey: 0.8; sports: 0.4; winter: 0.3. In some examples, user preferences can be provided as a feature vector. The numerical value is the probability of user interacting with an item on a specified topic. For example, a user vector can provide ice-hockey: 0.2; tennis: 0.6, which indicates that the particular user is much more likely to interact with items about tennis than with items about ice-hockey. In some examples, the relevance score is determined as a function of cross-product of the two vectors.

FIG. 5 depicts an example system 500 for ranking and ordering items displayed in a stream page. The example system 500 includes a first user 502 interacting with a first computing device 504, a second user 506 interacting with a second computing device 508, a first server system 510, a second server system 512 and a network 514. The first computing device 504, the second computing device 508, the first server system 510 and the second server system 512 can communicate with one another over the network 514.

In some implementations, a social networking service is provided and can be executed using the first and second server systems 510, 512. In some examples, the first server system 510 can receive, store and distribute items posted to the social networking service by users of the social networking service (e.g., the first user 502 and the second user 506). In some examples, the first server system 510 can monitor occurrences of events (e.g., item generation/distribution, comments, endorsements, sharing) and can determine which users are to receive the event (e.g., which users to deliver an item to). The first server system 510 can provide event data to the second server system. In some examples, the second server system 512 provides a scoring service. The scoring service can determine the scores discussed herein and can provide the scores to the first server system 510 for item ranking and ordering.

FIG. 6 depicts example ranking and ordering of example items (e.g., items sampled based on the ranks of authors and/or other items) in a stream page for a user (e.g., the first user 502 of FIG. 5). FIG. 6 includes example items 600, 602, 604, 606, 608, 610. Each item 600, 602, 604, 606, 608, 610 includes digital content data (P₁, P₂, P₃, P₄, P₅, P₆, respectively), item score (S_(1A), S_(2A), S_(3A), S_(4A), S_(5A), S₆, respectively) and timestamp (t₁, t₂, t₃, t₄, t₅, t₆, respectively). The item scores are specific to the first user (e.g., User A). The item information can be provided in an index of items that is specific to the first user. In some examples, the timestamps are dynamic in that each timestamp can change based on events associated with the item over time, as discussed herein. In some examples, the item scores are dynamic in that each item score can change based on events and social relationships between the first user and other users (e.g., item authors) over time, as discussed herein. In some implementations, current item score and timestamp data are retrieved in response to a request to display items to the user (e.g., when the user logs into the social networking service, when the user clicks on the stream page, when the user refreshes the stream page, when the stream page is automatically refreshed). In some implementations, the item score and timestamp data are periodically updated. In some implementations, data stored in the index of items is automatically updated when a timestamp and/or an item score changes.

With continued reference to FIG. 6, an initial collection of items 620 is provided. The initial collection of items 620 includes all items 600, 602, 604, 606, 608, 610. In some implementations, the items are filtered to provide filtered items 622. In some examples, the items can be filtered based on whether an item is considered to be undesirable. For example, each item can include data indicating whether the item is deemed to be spam. If a particular item is deemed to be spam, it can be filtered from being displayed to the first user. In some examples, an item can be deemed to be spam if the item is distributed to a number of users that exceeds a threshold number of users. In some examples, an item can be deemed to be spam if the item is distributed to a number of users that exceeds a threshold number of users and a social affinity score between the first user and the author of the item is below a threshold social affinity score (e.g., the author distributed the item to a large number of users and the first user has little, if any, social affinity to the author). In some examples, the items can be filtered based on item score. For example, each item score can be compared to a threshold item score and any items having an item score that is less than the threshold item score is filtered. In the example of FIG. 6, the item 606 is filtered. Consequently, the filtered items include items 600, 602, 604, 608, 610.

The filtered items 622 are grouped based on timestamp to provide grouped items 624. In the depicted example, the grouped items include a first item group 626 and a second item group 628. By way of non-limiting example, the first item group 626 can include items having timestamps within the last 24 hours and the second item group 628 can include items having timestamps within the last 1 to 2 days. Continuing with this example, items 602, 604, 608 are provided in the first item group 626. Consequently, timestamps t₂, t₃, t₅ fall within the last 24 hours, where t₂ is more recent than t₃ which is more recent than t₅. Items 600, 610 are provided in the second item group 628. Consequently, timestamps t₁, t₆ fall within the last 1-2 days, where t₁ is more recent than t₆.

The grouped items 624 are ranked based on item score within the groups to provide ranked items 630. In the depicted example, S_(5A) is greater than S_(3A) which is greater than S_(2A). Consequently, item 608 is ranked higher than item 604 and item 604 is ranked higher than item 602 even though t₂ is more recent than t₃ which is more recent than t₅. In the depicted example, S₆ is greater than S_(1A). Consequently, item 610 is ranked higher than item 600 even though t₁ is more recent than t₆. Accordingly, when the first user accesses the stream page the items will be displayed in the following order from top to bottom: item 608, item 604, item 602, item 610 and item 600. This order is specific to the first user, as illustrated further below.

FIG. 7 is a flow chart of an example process 700 that can be executed in implementations of the present disclosure. In some implementations, the example process 700 can include operations that are performed using one or more computer programs executed using one or more data processing apparatus (e.g., one or more client-side computing devices and/or one or more server-side computing devices). The example process 700 can be used for updating a timestamp of an item.

It is determined whether an item is posted (702). For example, an author of a social networking service can generate an item and can post the item to the social networking service, as discussed herein. The item can be distributed to one or more users of the social networking service. If an item is posted, an item timestamp (ts_(ITEM)) is set equal to the timestamp at which the item was posted to the social networking service (t_(POST)) (704). An item weight (w_(ITEM)) of the item is set equal to zero (706).

It is determined whether an event associated with the item is received (708). As discussed above, example events can include a comment to the item, an endorsement of the item and sharing of the item. If an event is not received, the process 700 loops back. If an event is received, w_(ITEM) is incremented based on the event (710). For example, and as discussed above, w_(ITEM) can be incremented based on event type and/or characteristics of the event. It is determined whether w_(ITEM) exceeds a threshold item weight (w_(THR)) (712). If w_(ITEM) exceeds w_(THR), ts_(ITEM) is set equal to the timestamp of the last event (ts_(EvENT)) (e.g., the event that resulted in incrementing w_(ITEM)) (716), and the process 700 loops back to reset w_(ITEM) back to zero (706).

If w_(ITEM) does not exceed w_(THR), it can be determined whether a timed out condition has occurred. In some examples, events associated with the item and updating of ts_(ITEM) is only performed for a threshold time (e.g., 1 month, 2 months, 3 months, 6 months, 1 year) from the occurrence of the last ts_(ITEM) update. In such examples, a timestamp difference (Δts) can be determined as a difference between a current time and ts_(ITEM). It can be determined whether Δts exceeds a threshold time (t_(THR)) (714). If Δts exceeds t_(THR), the example process 700 ends. If Δts does not exceed t_(THR), the example process 700 loops back.

FIG. 8 is a flow chart of an example process 800 that can be executed in implementations of the present disclosure. In some implementations, the example process 800 can include operations that are performed using one or more computer programs executed using one or more data processing apparatus (e.g., one or more client-side computing devices and/or one or more server-side computing devices). The example process 800 can be executed to order and rank items that are to be displayed to a user in a stream page.

One or more items (item₁, . . . , item_(n)) that are visible to user are identified (802). For example, each item is generated and posted to the social networking service by an author and includes a distribution that includes the user. Consequently, each item is accessible, or otherwise visible to the user, such that each item can be displayed in a stream page of the user. In some examples, the items are identified in response to the user logging into the social networking service and/or a user request to display items in the stream page of the user. Respective timestamps (ts) and item scores (S) are received for the items (804). In some examples, the timestamp and item score data is received from an item repository (e.g., database).

A counter j is set equal to 1 (806). It is determined whether an item score (S_(j)) is less than a threshold item score (S_(THR)) (808). The item score S₁ is the item score associated with item_(j) of the items item₁, . . . item_(n). If S_(j) is less than S_(THR), it is determined whether j is equal to n (e.g., whether the item scores for each of item₁, . . . , item_(n) have been considered) (810). If S_(j) is not less than S_(THR), item_(j) is added to a list of items that are too be displayed to the user (812). In this manner, items having an item score that is less than S_(THR) (e.g., items that are considered to be spam) are filtered from being displayed to the user. If j is not equal to n, j is incremented (814) and the process 800 loops back to compare the next item score to S_(THR). If j is equal to n, the list of items that are to be displayed to the user is complete.

Items in the list of items that are to be displayed to the user are grouped based on the respective timestamps (816). Items within each group are ranked based on the respective item scores (818). The items are displayed to the user in the stream page and are ranked and ordered in the display (820), and the process 800 ends.

FIG. 9 is a flow chart of an example process that can be executed in implementations of the present disclosure. In some implementations, the example process 900 can include operations that are performed using one or more computer programs executed using one or more data processing apparatus (e.g., one or more client-side computing devices and/or one or more server-side computing devices). The example process 900 can be executed to sample items that are to be displayed to a user in a stream page based on the rank of one or more authors.

A plurality of content items is obtained for a user based on the user's social network contacts (902). Each item comprises digital content and is associated with a respective author, a respective score, and a respective timestamp. A respective sampling rate is determined for each of a plurality of different first authors based on a respective rank assigned to the author by the user (904). For example, the user can assign a rank to author Sam by selecting the “show less” button 420 or the “show more” button 422 of stream page 400. One or more of the items are sampled for each of one or more of the first authors according to the respective sampling rate for the respective first author (906). Each of the sampled items has a respective author that is the first author, a respective score that is beyond a score threshold, and a respective timestamp that is within a given time period (906). The sampled items are then provided for presentation to the user (908) in, for example, a stream page (e.g., 400) of a social networking service.

A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. For example, various forms of the flows shown above may be used, with steps re-ordered, added, or removed. Accordingly, other implementations are within the scope of the following claims.

Implementations of the present disclosure and all of the functional operations provided herein can be realized in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the invention can be realized as one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. However, a computer readable medium is not a propagated signal. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this disclose can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the invention can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

Implementations of the invention can be realized in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

While this disclosure contains many specifics, these should not be construed as limitations on the scope of the disclosure or of what may be claimed, but rather as descriptions of features specific to particular implementations of the disclosure. Certain features that are described in this disclosure in the context of separate implementations can also be provided in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be provided in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Thus, particular implementations of the present disclosure have been described. Other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. 

What is claimed is:
 1. A method implemented by data processing apparatus, the method comprising: obtaining, by the data processing apparatus, a plurality of items for a user, each item comprising digital content and being associated with a respective author, a respective score, and a respective timestamp, wherein the respective score is at least partially based on a social affinity on a social networking service between the user and the respective author; receiving, by the data processing apparatus, user input from the user indicating a user-specific rank assigned to an author of a set of items of the plurality of items, the user-specific rank being specific to the user; determining, by the data processing apparatus, a sampling rate for the author based on the user-specific rank, the sampling rate indicating a proportion of the set of items of the author to be presented to the user; sampling, by the data processing apparatus, the set of items to provide a sub-set of items, wherein a number of the sub-set of items is based on the proportion of the set of items indicated by the sampling rate, and one or more items in the sub-set of items each have a respective timestamp that is within a given time period; and providing, by the data processing apparatus, the one or more items in the sub-set of sampled items for presentation to the user.
 2. The method of claim 1 wherein the sampling rate increases as the user-specific rank increases, and wherein the sampling rate decreases as the user-specific rank decreases.
 3. The method of claim 1 wherein the one or more items in the sub-set of items are each associated with one or more events.
 4. The method of claim 3 wherein an event comprises one of a comment on a particular item, an endorsement of the particular item, a muting of the particular item, or a share of the particular item.
 5. The method of claim 1 wherein items in the sub-set of items each pertain to one or more topics of interest to the user.
 6. The method of claim 1 wherein items in the sub-set of items for the author each have a respective score that is beyond a score threshold.
 7. The method of claim 1 wherein the respective score of a particular item is further based on, at least, a relevance score wherein the relevance score is determined based on a relevance of the particular item to the user.
 8. The method of claim 1, further comprising ordering items in the sub-set of items before providing the one or more items in the sub-set of items for presentation to the user, wherein the one or more items in the sub-set of items are ordered according to, at least, respective timestamps and respective scores of the items.
 9. The method of claim 1 wherein the digital content comprises digital content distributed using a computer-implemented social networking service.
 10. The method of claim 1 wherein the one or more items in the sub-set of items are presented to the user in a stream page of a social networking service.
 11. A system comprising: non-transitory computer readable media having instructions stored thereon; and data processing apparatus configured to execute the instructions to perform operations comprising: obtaining a plurality of items for a user, each item comprising digital content and being associated with a respective author, a respective score, and a respective timestamp, wherein the respective score is at least partially based on a social affinity on a social networking service between the user and the respective author; receiving user input from the user indicating a user-specific rank assigned to an author of a set of items of the plurality of items, the user-specific rank being specific to the user; determining a sampling rate for the author based on the user-specific rank, the sampling rate indicating a proportion of the set of items of the author to be presented to the user; sampling the set of items to provide a sub-set of items, wherein a number of the sub-set of items is based on the proportion of the set of items indicated by the sampling rate, and one or more items in the sub-set of items each have a respective timestamp that is within a given time period; and providing the one or more items in the sub-set of sampled items for presentation to the user.
 12. The system of claim 11 wherein the sampling rate increases as the user-specific rank increases, and wherein the sampling rate decreases as the user-specific rank decreases.
 13. The system of claim 11 wherein the one or more items in the sub-set of items are each associated with one or more events.
 14. The system of claim 13 wherein an event comprises one of a comment on a particular item, an endorsement of the particular item, a muting of the particular item, or a share of the particular item.
 15. The system of claim 11 wherein the items in the sub-set of items each pertain to one or more topics of interest to the user.
 16. The system of claim 11 wherein the items in the sub-set of items for the author each have a respective score that is beyond a score threshold.
 17. The system of claim 11 wherein the respective score of a particular item is further based on, at least, a relevance score wherein the relevance score is determined based on a relevance of the particular item to the user.
 18. The system of claim 11, wherein the operations further comprise ordering items in the sub-set of items before providing the one or more items in the sub-set of items for presentation to the user, wherein the one or more items in the sub-set of items are ordered according to, at least, respective timestamps and respective scores of the items.
 19. The system of claim 11 wherein the digital content comprises digital content distributed using a computer-implemented social networking service.
 20. The system of claim 11 wherein the one or more items in the sub-set of items are presented to the user in a stream page of a social networking service.
 21. Non-transitory computer readable media having instructions stored thereon that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: obtaining a plurality of items for a user, each item comprising digital content and being associated with a respective author, a respective score, and a respective timestamp, wherein the respective score is at least partially based on a social affinity on a social networking service between the user and the respective author; receiving user input from the user indicating a user-specific rank assigned to an author of a set of items of the plurality of items, the user-specific rank being specific to the user; determining a sampling rate for the author based on the user-specific rank, the sampling rate indicating a proportion of the set of items of the author to be presented to the user; sampling the set of items to provide a sub-set of items, wherein a number of the sub-set of items is based on the proportion of the set of items indicated by the sampling rate, and one or more items in the sub-set of items each have a respective timestamp that is within a given time period; and providing the one or more items in the sub-set of sampled items for presentation to the user.
 22. The computer readable media of claim 21 wherein the sampling rate increases as the user-specific rank increases, and wherein the sampling rate decreases as the user-specific rank decreases.
 23. The computer readable media of claim 21 wherein the one or more items in the sub-set of items are each associated with one or more events.
 24. The computer readable media of claim 23 wherein an event comprises one of a comment on a particular item, an endorsement of the particular item, a muting of the particular item, or a share of the particular item.
 25. The computer readable media of claim 21 wherein the items in the sub-set of items each pertain to one or more topics of interest to the user.
 26. The computer readable media of claim 21 wherein the items in the sub-set of items for the author each have a respective score that is beyond a score threshold.
 27. The computer readable media of claim 21 wherein the respective score of a particular item is further based on, at least, a relevance score wherein the relevance score is determined based on a relevance of the particular item to the user.
 28. The computer readable media of claim 21, wherein the operations further comprise ordering items in the sub-set of items before providing the one or more items in the sub-set of items for presentation to the user, wherein the one or more items in the sub-set of items are ordered according to, at least, respective timestamps and respective scores of the items.
 29. The computer readable media of claim 21 wherein the digital content comprises digital content distributed using a computer-implemented social networking service.
 30. The computer readable media of claim 21 wherein the one or more items in the sub-set of items are presented to the user in a stream page of a social networking service. 